Google’s DeepMind AI research team has unveiled a new open source AI model, Gemma 3 270M — far smaller than the 70 billion or more parameters of many frontier LLMs (parameters being the number of internal settings governing the model’s behavior). While more parameters generally translates to a larger and more powerful model, Google’s focus with this is nearly the opposite: high-efficiency, giving developers a model small enough to run directly on smartphones and locally, without an internet connection, as shown in internal tests on a Pixel 9 Pro SoC. Yet, the model is still capable of handling complex, domain-specific tasks and can be quickly fine-tuned in mere minutes to fit an enterprise or indie developer’s needs. Google DeepMind Staff AI Developer Relations Engineer Omar Sanseviero added that it Gemma 3 270M can also run directly in a user’s web browser, on a Raspberry Pi, and “in your toaster,” underscoring its ability to operate on very lightweight hardware. Gemma 3 270M combines 170 million embedding parameters — thanks to a large 256k vocabulary capable of handling rare and specific tokens — with 100 million transformer block parameters. According to Google, the architecture supports strong performance on instruction-following tasks right out of the box while staying small enough for rapid fine-tuning and deployment on devices with limited resources, including mobile hardware. One of the model’s defining strengths is its energy efficiency. In internal tests using the INT4-quantized model on a Pixel 9 Pro SoC, 25 conversations consumed just 0.75% of the device’s battery. This makes Gemma 3 270M a practical choice for on-device AI, particularly in cases where privacy and offline functionality are important. The release includes both a pretrained and an instruction-tuned model, giving developers immediate utility for general instruction-following tasks. Quantization-Aware Trained (QAT) checkpoints are also available, enabling INT4 precision with minimal performance loss and making the model production-ready for resource-constrained environments. Google frames Gemma 3 270M as part of a broader philosophy of choosing the right tool for the job rather than relying on raw model size. For functions like sentiment analysis, entity extraction, query routing, structured text generation, compliance checks, and creative writing, the company says a fine-tuned small model can deliver faster, more cost-effective results than a large general-purpose one. By fine-tuning a Gemma 3 4B model for multilingual content moderation, the team outperformed much larger proprietary systems. Gemma 3 270M is designed to enable similar success at an even smaller scale, supporting fleets of specialized models tailored to individual tasks.